抄録
This paper applies a method, Genetic Algorithm with Search Area Adaptation (GSA), to the function optimization. In Previous study, GSA has proposed for the floorplan design problem and it has shown better performance than several existing methods. We believe that investigation of the searching behavior of the algorithm is important. However, since the floorplan design problem is combinatorial optimization problem, we do not know in detail why GSA works well. To study details of the searching behavior, we believe GSA should be applied to a problem in which several benchmarks whose optima and landscapes are known have been proposed and an appropriate measure of distance between solutions can be defined. In this paper, we apply GSA to the function optimization, which satisfies the requirements. There is another purpose to apply GSA to the function optimization. We would like to propose a superior method for the function optimization. through several experiments, we have confirmed that GSA works adaptively along with the searching stage and it shows higher and stabler performance than one of existing methods.
